Cognito iQ has been helping companies capture data from the field through our mobile applications for over 20 years. Today, we work with Facilities Management (FM) companies to help them with one of the biggest challenges in the industry: getting the best from their distributed workforce.
All of our customers collect data from their field workers, and all are striving to do more with their data. They are at varying points along a path, which we have called the data maturity journey, the end point of which is where we find the most highly evolved organisations - those that are getting the maximum measurable value from their data, in terms of improved business performance.
The data maturity journey
Stage one of the journey is collecting data. Stage two is using that data to keep on top of your service operation, for example by keeping an accurate record of the number of jobs completed each day. Stage three is using that data to spot patterns, and make changes to your business processes to improve service. This stage is typically where many FM businesses are today: collecting, storing and occasionally analysing data, but with much more potentially valuable data just building up and overpopulating back-office systems.
Getting to stage four
How would it be if you could uncover the insights that are hiding in data, if you could know how to find them and when to act on them to improve your service levels? We believe that this capability, stage four of the data maturity journey, can transform organisations from reactive environments, being managed by static and old data, to automated continuous learning environments.
Despite the value of this information, many FM leaders today don’t attain the potential benefits. Management practices today often rely on lagging metrics, retrospectively analysing forecasts and actuals in hindsight – management in the rear-view mirror.
Most companies already have the data in their CRM, ERP or other operational systems, but the challenge is accessing it in a timely fashion. Our conversations with leading FM organisations tell us that data is delivered in a form which doesn’t support decision-making. Management teams have to spend time gathering and aligning data from disparate sources, and formatting it so they can use it. Time spent sifting through data is time away from the frontline of the operation, with engineers and customers.
For FM leaders to gain maximum benefit from their decision support systems, they need information consolidated and in real-time. Big data technologies are making this possible. With the onslaught of Cloud providers and an ever increasing library of applications, all of a sudden, new and creative ways to gain insights are available.
Real time analytics in action
There are many industries leading the way in real-time data analytics, which are disrupting and revolutionising our professional and consumer lifestyles. For example, Samsung has announced that they will be putting visual real-time analytics into their smartphones. Cameras, when combined with real-time analytics, make your smartphone even smarter. Imagine applications such as scanning your dinner in a restaurant to find out the ingredients of your meal and the calorie count.
Other examples of real-time analytics come from the world of sport. Professional tennis is a game of geometry and percentages. As a match progresses, patterns start to surface. A slight increase in unforced errors or first-serve success can mean the difference between winning and losing. However, tennis, has been slow to adopt real-time analytics, with other sports such as cycling and American football leading the way. In tennis, coaches have just watched from the stands, and can advise their player during breaks in all but Grand Slam matches. But now, tennis coaches are starting to use data to gain a range of real-time and historic analysis. Getting context around key statistics during and prior to a match helps a coach understand the strengths and weaknesses of an individual player, and also their opponent. A coach can take a tablet on to court and show their player their opponent’s serve direction, placement on the court, contact point for returning a serve and placement of rally shots, allowing them to exploit their weak side.
In healthcare, real-time data can allow earlier identification of patients with deteriorating health, free up nurses’ time and help hospitals to better plan staff and resource deployment. Technologies such as wearable devices, sensors and other patient monitoring devices can help those with long term illnesses to continue living at home for longer. Sensors can be placed around the home, in appliances or on the patient’s body, enabling care givers to track and monitor movements through smartphone apps which send alerts when the patient is at risk, for example, missing a meal, falling or failing to get out of bed.
Our NHS is well positioned to take advantage of big data and real-time analytics because the personal data for each patient already exists, and is already more centralised than in other countries. For example, nurses in 50 hospitals across England use one electronic system, VitalPAC, to record five million patient observations a month, using iPads.
These examples show how big data and real-time analytics are enhancing industries and improving our lifestyles. Companies with mobile workforces have a history of innovation and are well placed to capitalise on these technologies. Field engineers were the first to use handheld devices to improve business processes when out on the road, and the FM industry has reaped the benefits of increased productivity, happier customers and a more motivated workforce. These three areas are more important today than ever, as companies look for points of differentiation. Real-time analysis, combined with business KPIs, provides a deep understanding of operational patterns over time, enabling decision makers to detect weak signals early, giving more time for corrective measures and alternative outcomes.
Productivity has many facets including how well your people are being utilised, how effective they are, how consistent and how compliant. To increase productivity you need to know what productivity means to your business, and where there are areas of give and take. For example, you may want to increase the number of completed tasks in a shift, but would you be happy to compromise first time fix rates? Probably not. But you may wish to reduce the number of hours spent travelling to jobs, or to ensure that engineers are sufficiently trained and equipped to do the work allocated to them. If you have data that shows, in real time, the status of all engineers on shift and all allocated jobs, the first time fix rate, and SLA compliance rate, you can allocate resources more effectively. For example if a customer calls to say they can’t make their appointment, you can easily reallocate resource to respond to emergency or unplanned work, making the shift more productive. Over time, you can analyse the extent to which this happens and adjust your planning accordingly.
For some years now, businesses of all types have attempted to differentiate by giving their customers improved experiences. Traditionally, the FM industry has been more focused on keeping costs down, but research suggests that FM companies are now putting the customer first. Managing customer experiences means paying attention to every interaction the customer has with your company. In this context, it becomes clear that a visit from a field service engineer is loaded with opportunities to delight – or disappoint – the customer. If you have data that shows you in real time which tasks are at most risk of breaching their SLA, such as those that are still travelling to the job, or those that are not yet on the way, then you can take action by talking to the Engineer, finding another available engineer or calling the customer to explain the situation and understand the customer expectation. Over time, you can adjust the plan to improve your SLA compliance rate, driving up customer satisfaction.
As FM business models change to better meet customers’ needs, for example from reactive to predictive maintenance, employees may struggle to adapt. This can have a detrimental effect on customer satisfaction. If your employees are disengaged, frustrated or unhappy, they won’t help you accomplish your service delivery goals. This issue is compounded by the aging engineering workforce. Organisations are facing recruitment issues, as well as the challenge of managing, training and motivating a disparate workforce of older technicians and their millennial replacements.
If you have data on employee performance on a number of metrics, you can identify issues during each shift and speak to engineers to resolve any problems they may be experiencing. Over time it enables you to set expectations on KPIs, which gives your workforce something to work towards. It allows managers to spot under- and over-performers, training gaps and promotion opportunities.
The way forward for FM
At most FM and field service organisations today, the lack of real-time visibility results in a reactive approach to managing operations and staff. Managers don’t use data to solve problems, but rely on intuition instead. This is because service leaders don’t have access to real-time data and analytics that enable them to make decisions based on facts.
Key use cases for analytics in FM include the following:
- Analysing equipment monitoring data to automate maintenance scheduling
- Using predictive analytics to reduce reactive maintenance
- Analysing equipment performance data to inform purchasing decisions
- Preventing SLA breaches before they happen
- Monitoring customer feedback and resolving issues quickly
- Maximising employee utilisation, efficiency and effectiveness
Business First Magazine tells us that by 2020, we will be producing more than 1MB of new information every second for every human being on the planet. Yet, less than 0.5 percent of all this data is ever analysed and used. To succeed in FM, we must capture it, analyse it and use it - in real-time.